add data prepare for speechbrain format.

examples/aishell/asr3/conf/train_with_wav2vec.yaml

@@ -25,9 +25,7 @@
 # Authors:  Yingzhi WANG 2022
 # ############################################################################
 
-seed: 2
-__set_seed: !apply:torch.manual_seed [!ref <seed>]
-output_folder: !ref data/<seed>
+output_folder: !ref data
 cer_file: !ref <output_folder>/cer.txt
 save_folder: !ref <output_folder>/save
 train_log: !ref <output_folder>/train_log.txt
@@ -39,7 +37,7 @@ skip_prep: False
 ckpt_interval_minutes: 15 # save checkpoint every N min
 train_data: !ref <output_folder>/train.csv
 valid_data: !ref <output_folder>/dev.csv
-test_data: !ref <output_folder>/dev_for_test.csv
+test_data: !ref <output_folder>/test.csv
 
 wav2vec2_hub: TencentGameMate/chinese-wav2vec2-large
 

examples/aishell/asr3/local/aishell_prepare.py

@@ -0,0 +1,129 @@
+# Copyright (c) 2023 speechbrain Authors. All Rights Reserved.
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# Modified from speechbrain 2023
+# (https://github.com/speechbrain/speechbrain/blob/develop/recipes/AISHELL-1/aishell_prepare.py)
+import argparse
+import csv
+import glob
+import logging
+import os
+
+from paddlespeech.s2t.models.wav2vec2.io.dataio import read_audio
+
+logger = logging.getLogger(__name__)
+
+DATA_HOME = os.path.expanduser('~/.cache/paddle/dataset/speech')
+
+parser = argparse.ArgumentParser(description=__doc__)
+parser.add_argument(
+    "--data_folder",
+    default=DATA_HOME + "/Aishell",
+    type=str,
+    help="Directory to save the dataset. (default: %(default)s)")
+parser.add_argument(
+    "--save_folder",
+    default="data/",
+    type=str,
+    help="Filepath prefix for output manifests. (default: %(default)s)")
+parser.add_argument(
+    "--skip_prep",
+    default=False,
+    type=bool,
+    help="If True, skip data preparation. (default: %(default)s)")
+args = parser.parse_args()
+
+
+def prepare_aishell(data_folder, save_folder, skip_prep=False):
+    """
+    This function prepares the AISHELL-1 dataset.
+    If the folder does not exist, the zip file will be extracted. If the zip file does not exist, it will be downloaded.
+    data_folder : path to AISHELL-1 dataset.
+    save_folder: path where to store the manifest csv files.
+    skip_prep: If True, skip data preparation.
+    """
+    if skip_prep:
+        return
+
+    # Create filename-to-transcript dictionary
+    filename2transcript = {}
+    with open(
+            os.path.join(data_folder,
+                         "data_aishell/transcript/aishell_transcript_v0.8.txt"),
+            "r", ) as f:
+        lines = f.readlines()
+        for line in lines:
+            key = line.split()[0]
+            value = " ".join(line.split()[1:])
+            filename2transcript[key] = value
+
+    splits = [
+        "train",
+        "dev",
+        "test",
+    ]
+    ID_start = 0  # needed to have a unique ID for each audio
+    for split in splits:
+        new_filename = os.path.join(save_folder, split) + ".csv"
+        if os.path.exists(new_filename):
+            continue
+        logger.info("Preparing %s..." % new_filename)
+
+        csv_output = [["ID", "duration", "wav", "transcript"]]
+        entry = []
+
+        all_wavs = glob.glob(
+            os.path.join(data_folder, "data_aishell/wav") + "/" + split +
+            "/*/*.wav")
+        for i in range(len(all_wavs)):
+            filename = all_wavs[i].split("/")[-1].split(".wav")[0]
+            if filename not in filename2transcript:
+                continue
+            signal = read_audio(all_wavs[i])
+            duration = signal.shape[0] / 16000
+            transcript_ = filename2transcript[filename]
+            csv_line = [
+                ID_start + i,
+                str(duration),
+                all_wavs[i],
+                transcript_,
+            ]
+            entry.append(csv_line)
+
+        csv_output = csv_output + entry
+
+        with open(new_filename, mode="w") as csv_f:
+            csv_writer = csv.writer(
+                csv_f, delimiter=",", quotechar='"', quoting=csv.QUOTE_MINIMAL)
+            for line in csv_output:
+                csv_writer.writerow(line)
+
+        msg = "\t%s successfully created!" % (new_filename)
+        logger.info(msg)
+
+        ID_start += len(all_wavs)
+
+
+def main():
+    if args.data_folder.startswith('~'):
+        args.data_folder = os.path.expanduser(args.data_folder)
+
+    prepare_aishell(args.data_folder, args.save_folder, skip_prep=False)
+
+    print("Data csv prepare done!")
+
+
+if __name__ == '__main__':
+    main()

examples/aishell/asr3/local/data.sh

@@ -1,7 +1,7 @@
 #!/bin/bash
 
 stage=-1
-stop_stage=100
+stop_stage=-1
 dict_dir=data/lang_char
 
 . ${MAIN_ROOT}/utils/parse_options.sh || exit -1;
@@ -17,6 +17,12 @@ if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
     --manifest_prefix="data/manifest" \
     --target_dir="${TARGET_DIR}/aishell"
 
+    #generate csv file for speechbrain dataloader
+    python3 local/aishell_prepare.py \
+    --data_folder="${TARGET_DIR}/aishell" \
+    --save_folder="data/"
+
+
     if [ $? -ne 0 ]; then
         echo "Prepare Aishell failed. Terminated."
         exit 1